ADR-Lite A Low-Complexity Adaptive Data Rate Scheme for LoRa Network Reza Serati Benyamin Teymuri Nikolaos Athanasios Anagnostopoulosy and Mehdi Rasti

2025-05-06 0 0 918.2KB 6 页 10玖币

侵权投诉

ADR-Lite: A Low-Complexity Adaptive Data Rate

Scheme for LoRa Network

Reza Serati∗, Benyamin Teymuri∗, Nikolaos Athanasios Anagnostopoulos†, and Mehdi Rasti∗,∗∗

∗Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

†Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany

∗∗Centre for Wireless Communications, University of Oulu, Finland

∗Emails: {re.serati, benyamin.teymuri, rasti}@aut.ac.ir

†Email: Nikolaos.Anagnostopoulos@uni-passau.de ∗∗Email: mehdi.rasti@oulu.ﬁ

Abstract—The Internet of Things (IoT) is currently used for

various applications, including smart cities, agriculture, and

smart homes. The IoT applications’ long-range and low energy

consumption requirements have led to a new wireless commu-

nication technology known as Low Power Wide Area Network

(LPWANs). In recent years, the Long Range (LoRa) protocol has

gained a lot of attention as one of the most promising technologies

in LPWAN. Choosing the right combination of transmission

parameters is a major challenge facing the LoRa network. LoRa

executes an Adaptive Data Rate (ADR) mechanism to conﬁgure

each End Device’s (ED) transmission parameters, resulting in

improved performance metrics. In this paper, we propose a

linkbased ADR approach that aims to conﬁgure the transmission

parameters of EDs by making a decision without taking into

account the history of the last received packets, resulting in a rel-

atively low space complexity approach. In this study, we present

four different scenarios for assessing performance, including a

scenario where mobile EDs are considered. Our simulation results

show that in a mobile scenario with high channel noise, our

proposed algorithm’s Packet Delivery Ratio (PDR) is 2.8 times

outperforming the original ADR and 1.35 times that of other

relevant algorithms.

Index Terms—IoT, LPWAN, LoRa, adaptive data rate (ADR),

mobile devices, energy consumption.

I. INTRODUCTION

A consistent low-cost and low-energy connectivity amongst

all smart devices is required to build an intelligent society [1].

In the Internet of Things (IoT) environment, Low Power Wide

Area Networks (LPWANs) are developed for energy con-

sumption optimization and improved communications range.

The Packet Delivery Ratio (PDR), Energy Consumption (EC),

resilience in the face of faults and challenges, and coverage

area are some measures that may be used to assess a network’s

performance. Environmental conditions such as urban (UR)

and suburban (SU) conditions, the number of transmitting end

devices (EDs), the number and placement of Gateways (GWs),

network topology, and regulatory restrictions are salient factors

that can directly inﬂuence network functionality [2]. The LoRa

network is a low-power, long-range communication protocol

that can cover a wide distance. To establish a communication

link, a set of transmission parameters have to be conﬁgured.

Transmission parameters such as the Spreading Factor (SF),

Transmission Power (TP), Carrier Frequency (CF), Bandwidth

(BW), and Coding Rate (CR) can be conﬁgured in a LoRa

network to ensure reliable communication.

Combining the transmission parameters provides a state

space from which hundreds of conﬁgurations can be cho-

sen, impacting the network performance [3]. Choosing the

right combination of transmission parameters is a major

challenge facing the LoRa network. In the central decision-

making Network Server (NS), LoRa executes an Adaptive

Data Rate (ADR) mechanism to conﬁgure EDs’ transmission

parameters, resulting in improved performance metrics. To

increase the efﬁciency and scalability of LoRa networks, nu-

merous articles with different approaches have been published.

Changes in Media Access Control (MAC) [4], the number

of message retransmissions [5], statistical and mathematical

models [6],optimization algorithms [7], and machine learning

techniques [8] are among the approaches discussed. This paper

aims to review, implement, and analyze a new greedy approach

while maintaining minimal space complexity. This approach

can improve network performance in terms of reducing the

collision rate and thereby increasing PDR. Contributions made

by this work are as follows:

•Our proposed Adaptive Data Rate Low-complexity

scheme, ADR-Lite, conﬁgures the transmission parame-

ters of the LoRa network in variable channel conditions,

independent of the EDs’ number and distribution, whether

they are static or on the move. This is achieved while

our algorithm’s space complexity remains optimized com-

pared to other approaches.

•Unlike existing approaches, which are limited to set-

ting SF and TP only, our suggested algorithm includes

adjusting SF, TP and other transmission parameters such

as CF and CR. Thus, ADR-Lite offers a greater set of

conﬁguration parameters than other ADR schemes, mak-

ing it more ﬂexible and adaptable to various deployments

and requirements.

•Our simulation results show that our proposed ADR-Lite

improve the ratio of total consumed energy by all EDs to

the PDR, in different scenarios.

The rest of this paper is structured as follows. The back-

ground and related works are presented in Section II. Section

arXiv:2210.14583v1 [cs.NI] 26 Oct 2022

III describes our suggested greedy algorithm. The simulation

results and conclusion are included in sections IV and V,

respectively.

II. BACKGROUND AND RELATED WORKS

This section reviews the LoRa physical layer and transmis-

sion parameters. Then, it examines the related works.

A. LoRa overview

The LoRa protocol is a proprietary technology for a long-

range, low-power network. This method employs the Chirp

Spread Spectrum (CSS), which is one of the spread spectrum

modulations. LoRa wireless devices have become an important

part of the wireless IoT infrastructure. It has low efﬁciency in

terms of bits per second, while it can transmit no more than

255 bytes of data per packet, which is sufﬁcient for many

IoT applications [9]. The LoRa architecture employs the star-

of-star topology, which has three types of devices seen in

Figure 1.

LoRa makes use of unlicensed sub-gigahertz radio fre-

quency bands like the 433, 868, or 915 MHz industrial,

scientiﬁc, and medical (ISM) bands, as determined by the

region it is deployed in [2]. There are ﬁve transmission pa-

rameters that can be conﬁgured for appropriate sender-receiver

communication, impacting communication link quality. The

descriptions of these parameters are listed below [2]:

•SF: In the spread spectrum LoRa modulation, each bit

of the payload message represents multiple chips of

information. The symbol rate is the rate at which the

spread information is sent. SF is the ratio of the nominal

symbol rate to the chip rate, which represents the number

of symbols transferred per bit of data. The SF can be

selected from 7 to 12. Note that since different spreading

factors are orthogonal to one another, the SF must be

known in advance on both the transmitting and the

receiving sides of the link.

•TP: The amount of power that an ED should put in to

transfer its messages is known as transmission power. The

LoRa radio TP may vary in steps of 3dBm from 2to 14

dBm.

•CF: The central frequency, measured in Hertz, of a carrier

wave that is modulated to transmit signals, is known

as the carrier frequency. CF may be conﬁgured in the

frequencies of 433, 868, and 915 MHz with different

step sizes depending on the LoRa chip and the regulation

rules.

•BW: Bandwidth is the frequency range between the low-

est and highest frequencies that can be reached without

causing signal power degradation. Increasing the signal

bandwidth allows for a higher data rate and lower trans-

mission time at the expense of reducing sensitivity. LoRa

employs the bandwidth ranges of 125 kHz, 250 kHz, and

500 kHz.

•CR: To enhance the link’s robustness even further, LoRa

uses cyclic error coding for forward error detection and

correction. Such error coding increases the transmission

Fig. 1. LoRaWAN network architecture.

overhead, reducing the data rate and improving the link’s

reliability in the presence of interference. The CR, and

hence the interference resistance, may be changed accord-

ing to the channel conditions. The values of CR may vary

in the range of {4

5,4

6,4

7,4

8}.

The combination of these parameters can affect the data

rate, noise resistance, receiver sensitivity, packet transmission

delay, and energy consumption. For example, if ED is con-

ﬁgured to transmit with the maximum SF, i.e., SF12, the

most extended coverage area, the highest energy consumption,

the longest transmission delay, and the lowest data rate will

be achieved [3]. Consequently, ﬁnding the best combination

of transmitting parameters is one of the main challenges of

optimizing LoRa networks to reduce power consumption and

collision rates while increasing PDR.

B. Related Works

Many relevant works have attempted to improve LoRa net-

work performance [3], [7], [8], [10], [11]. Examining the full

transmission parameter state space to ﬁnd the best combination

is an exhaustive approach. A probing approach is described

in [3], with the objective of ﬁnding an appropriate combination

with the least amount of state space exploration. Along with

minimizing the size of the space state, the suggested strategy

also considers lowering energy usage.

The approaches available for controlling and managing the

transmission parameters in the LoRa network are divided

into two categories, network-aware [8] and link-based ap-

proaches [7], [10]. The transmission parameters between the

EDs and the GWs are determined by the NS in a centralized

manner (link-based approach), whereas the transmission pa-

rameters are determined in a distributed manner by the EDs,

in the network-aware approach.

The ADR algorithm is a mechanism to adjust the trans-

mission parameters of EDs that not only can reduce the

ED’s energy consumption but also results in better PDR. The

default ADR method, which is a link-based approach known

as ADR-MAX, uses the maximum value of the last 20 packets’

Signal to Interference and Noise Ratio (SINR) as an indicator

文档加载中……请稍候！
如果长时间未打开，您也可以点击刷新试试。

下载文档到电脑，查找使用更方便

10 玖币 0人已下载

立即下载

摘要：

ADR-Lite:ALow-ComplexityAdaptiveDataRateSchemeforLoRaNetworkRezaSerati,BenyaminTeymuri,NikolaosAthanasiosAnagnostopoulosy,andMehdiRasti;DepartmentofComputerEngineering,AmirkabirUniversityofTechnology,Tehran,IranyFacultyofComputerScienceandMathematics,UniversityofPassau,Passau,GermanyCentrefo...

展开>> 收起<<

ADR-Lite A Low-Complexity Adaptive Data Rate Scheme for LoRa Network Reza Serati Benyamin Teymuri Nikolaos Athanasios Anagnostopoulosy and Mehdi Rasti.pdf

共6页,预览2页

还剩页未读，继续阅读

声明：本站为文档C2C交易模式，即用户上传的文档直接被用户下载，本站只是中间服务平台，本站所有文档下载所得的收益归上传人(含作者)所有。玖贝云文库仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对上载内容本身不做任何修改或编辑。若文档所含内容侵犯了您的版权或隐私，请立即通知玖贝云文库，我们立即给予删除！

ADR-Lite A Low-Complexity Adaptive Data Rate Scheme for LoRa Network Reza Serati Benyamin Teymuri Nikolaos Athanasios Anagnostopoulosy and Mehdi Rasti

相关推荐

开通VIP享超值会员特权

作者详情

相关内容

热门标签

举报选择: